Page 147 - IJOCTA-15-1

Modeling the renewable energy development in T¨urkiye with optimization
economic downturn and lack of available financ- T¨urkiye’s efforts in diversifying the electricity mix
ing. Despite the difficulties stated above, in the are also reflected in the renewable energy tar-
same year, T¨urkiye introduced its first net meter- gets and therefore potential utilization. Table
ing programme for solar systems under 10 kW of 1 shows the technical renewable energy potential
capacity. 9 in T¨urkiye for each technology (obtained from, 10)
in comparison with the installed capacities (ob-
3.4. Hydropower tained from IRENA database) 20 and the RE tar-
gets (obtained from.) 21 The current potential uti-
In 2017 and 2018, T¨urkiye installed a significant lization levels show that T¨urkiye needs to uti-
amount of hydropower. This is evident from the lize its renewable energy resources more efficiently
fact that, T¨urkiye ranked 5 th globally in terms and that the 2023 targets related with biomass
of net hydropower capacity additions in 2017 and and geothermal have already been reached.
4 th in 2018, in the same category. Hydropower
capacity in T¨urkiye has reached a year-end to- 4. Problem setup
tal of almost 28.3 GW in 2018. According to, 8
following a drought in 2017, hydropower genera- 4.1. Data collection
tion rebounded 5.5% to 60.9 TWh, and provided
more than 20% of the country’s electricity sup- In order to develop the models mentioned in this
ply for 2018. In 2019, T¨urkiye added 0.2 GW of section, various data had to be collected and an-
capacity, for a year-end total of 28.5 GW. Due alyzed. Firstly, the installed renewable electricity
to improved hydrological conditions in that year, capacity data (MW) have been retrieved from. 22
hydropower generation increased by nearly half In order to determine the parameters that will
to 88.8 TWh, providing around 30% of T¨urkiye’s be used for modeling the renewable energy devel-
total electricity supply. 9 opment in T¨urkiye, datasets regarding many dif-
ferent parameters (parameters about demograph-
ics, energy, environment, etc.) have been ob-
3.5. Bioenergy
tained. After that, those data have been individu-
According to, 18 there are more than 350 biomass ally tested in order to see if they have any relation
energy power plants (BEPPs) in T¨urkiye and with renewable energy development. Those pa-
most of the existing BEPPs use solid waste. rameters which have shown relation are used for
These power plants use different biomass sources developing models. These parameters will be re-
as fuel, such as; biowaste, solid waste, sludge, an- ferred as modeling parameters in this study. The
imal manure, and agricultural waste. The study modeling variables used in this study are listed
also reports that the cities which are more indus- below along with their units:
trialized (such as Ankara, Samsun, Mardin), seem (1) Domestic credit to private sector by banks
to have BEPPs with high installed capacity. A (% of GDP)
quick analysis of the data provided in 19 reveals
(2) GDP per capita (current US$)
that the installed electricity capacity related with
(3) Population, total
solid biofuels has increased more than 620% be-
(4) Urban population (% of total population)
tween 2017-2021. During the same period, the
(5) Researchers in R&D (per million people)
installed electricity capacity related with biogas
(6) Net energy imports (PJ)
has increased more than 160%. Although the rate (7) Coal Imports (TJ)
of increase seems to be smaller, according to, 19
(8) Natural gas final consumption (TJ-gross)
in 2021, the biogas related installed capacity was
(9) Electricity consumption (TWh)
close to 1000 MW while the solid biofuels related (10) CO 2 emissions per capita (tCO 2 per
installed capacity was slightly over 600 MW.
capita)
3.6. Comparative analysis The data for the modeling parameters stated
between 1-5 were obtained from Worldbank
In the recent years, the share of hydro has de- database 24 for T¨urkiye, while the data for the rest
clined while the share of geothermal, wind, and of the modeling parameters 6-10 were obtained
solar has increased. This can clearly be seen from from IEA database. 25-29 The correlations between
Figure 1 (constructed by using the data collected the related modeling variable and installed renew-
from), 20 which illustrates the share of each tech- able electricity capacity data are calculated by
nology in total installed renewable energy capac- using the data between 2005-2019. The related
ity. This actually shows that T¨urkiye is trying to correlations are; 0.90, 0.23, 0.98, 0.97, 0.97, 0.95,
diversify its energy mix. 0.97, 0.97, 0.98, 0.91 for the modeling variables
141